Parameter-induced uncertainty quantification of soil N₂O, NO and CO₂ emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

  • Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes.

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Author:Karl-Heinz Rahn, Christian WernerORCiD, Ralf Kiese, Edwin Haas, Klaus Butterbach-BahlORCiDGND
URN:urn:nbn:de:hebis:30:3-271434
DOI:https://doi.org/10.5194/bg-9-3983-2012
ISSN:1726-4189
ISSN:1726-4170
Parent Title (English):Biogeosciences
Publisher:Copernicus-Ges.
Place of publication:Katlenburg-Lindau
Document Type:Article
Language:English
Date of Publication (online):2012/10/17
Date of first Publication:2012/10/17
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2012/12/19
Volume:9
Issue:10
Page Number:16
First Page:3983
Last Page:3998
Note:
© Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.
HeBIS-PPN:315858761
Institutes:Angeschlossene und kooperierende Institutionen / Senckenbergische Naturforschende Gesellschaft
Fachübergreifende Einrichtungen / Biodiversität und Klima Forschungszentrum (BiK-F)
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 3.0